Titlebook: Swarm Intelligence; 13th International C Marco Dorigo,Heiko Hamann,Christian Camacho-Villal Conference proceedings 2022 Springer Nature Swi

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,Automatic Design of Multi-objective Particle Swarm Optimizers, As happens with other metaheuristics, finding the most adequate parameters settings for MOPSOs is not a trivial task, and it is even harder to choose structural components that determine the algorithm’s design. Thus, it is an open question whether automatically-designed MOPSOs can outperform the be

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,Benchmarking Performances of Collective Decision-Making Strategies with Respect to Communication Baable decentralized and localized decision-making behaviors in intelligent swarms. However, many proposed strategies have very different requirements on the communication bandwidth and paradigm, which make a clear and fair comparison difficult. In this paper, we seek to investigate the performances o

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,Best-of-N Collective Decisions on a Hierarchy,native distinctly shines over the others. Additionally, if the quality of the available alternatives is not a priori known and noisy, errors in the quality estimation may lead to the premature selection of sub-optimal alternatives. A typical speed-accuracy trade-off must be faced, which is hardened

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,Decentralized Multi-Agent Path Finding in Warehouse Environments for Fleets of Mobile Robots with Lthe related multi-agent path finding (MAPF) problem. However, most of the proposed MAPF algorithms still rely on centralized planning as well as simplistic assumptions, such as that robots have full observability of the environment and move at equal and constant speeds. The resultant plans thus cann

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,Dynamic Spatial Guided Multi-Guide Particle Swarm Optimization Algorithm for Many-Objective Optimizpdate of a particle, choosing the least crowded solution of a static number of solutions in the external archive. This report aims to determine the feasibility of utilizing a linearly decreasing tournament size with the aim of improving initial exploration and final exploitation of the search space

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,Extracting Symbolic Models of Collective Behaviors with Graph Neural Networks and Macro-Micro Evoluaviors are factors that make it difficult to distill a collective behavior into simple symbolic expressions. In this paper, we propose a novel approach to symbolic regression designed to facilitate such modeling. Using raw and post-processed data as an input, our approach produces viable symbolic ex